Energy-aware wireless mesh network deployment using optimization mechanism

TELKOMNIKATelecommunication,Computing,ElectronicsandControl

Vol.21,No.1,February2023,pp.26∼40

Energy-awarewirelessmeshnetworkdeploymentusing optimizationmechanism

AphirakJansang,ChayathornSimasathien,AnanPhonphoem

IntelligentWirelessNetworkGroup(IWING),DepartmentofComputerEngineering,KasetsartUniversity,Chatuchak,Bangkok10900, Thailand

ArticleInfo

Articlehistory:

ReceivedFeb22,2022 RevisedJul26,2022 AcceptedAug10,2022

Keywords: Energyconsumption Energy-awaredeployment Optimization Routerplacement Wirelessmeshnetwork Wirelessnetworkplanning

ABSTRACT

Wirelessmeshnetworksarewidelyusedtocreatenetworkinfrastructureinruralareasduetotheirflexibleproperties,suchasself-healingmechanismsandassociated redundantpaths.Forexample,awirelessmeshnetworkcansuitablyoperatewith limitedbatterypowerinwildlifemonitoringapplications.Thelocationsofmobile routerstosupportmobilesensornodesareessentialforextendingthesystemlifetime. Thisstudyproposesanenergy-awarewirelessmeshnetworkdeploymentoptimizationmechanism.Thegoalistodetermineasuitablelocationforthemeshrouters withtheaimofmaximizingnetworklifetime.Afterthelocationsolutionisobtained fromtheproposedmechanism,itisthenevaluatedforsystemlifetimeandnetwork performancebythenetworksimulator(ns-3).Theproposedmethodoutperformsthe brute-forcemethodintermsofcomputationtimeforallamountsofmeshclients.For example,for30meshclients,theproposedmethodusesonlyafewminutes,while thebrute-forcemechanismrequiresmorethan200minutestocompletetheprocess. Furthermore,comparedtothebrute-forcemethod,itachievesnearlythesamesystem lifetimeandotherperformanceparameters,suchasthroughput,packetdeliveryratio, andpacketinter-arrivaltime.Intherealimplementation,inwhichthesensornode placementscanbechangedduringtheinstallationperiodowingtotheenvironmental statusortherecommendationoftheinstallers,theresultscanberecalculatedinashort period.

AnanPhonphoem IntelligentWirelessNetworkGroup(IWING),DepartmentofComputerEngineering,KasetsartUniversity Chatuchak,Bangkok10900,Thailand Email:anan.p@ku.ac.th

1. INTRODUCTION

Currently,Internetaccessiswidelydeployedinbothurbanandruralareas.Highaccessspeedsin therangeofgigabitpersecondcanbeachievedowingtotheavailabilityoffibretothehome(FTTH)and asymmetricdigitalsubscriberline(ADSL)inurbanareas.Thetechnologiesareforfixedlinelocationandtoo costlyforlongcableinstallationthatarenotsuitableforremotescatteringlocationsinruralareas.Conversely, wirelesscellulartechnologies,suchas3GandLTE,aremorepopularduetotheircoverageareasandmobility support.HighspeedWi-Fiisananothersolutionthatisusedinhouseholdandpublicareas.However,its coverageareaislimitedupto50m–100m.

Forwildlifemonitoringapplications[1],[2],suchastigermonitoringorforestprotection[3],wireless communicationsarerequiredforreal-timeandnearreal-timemonitoring.Inlargewildlifesanctuaryareas,such asHuaiKhaKhaenglocatedinUthaiThaniandTakprovinces,Thailand,servicesofcellulartechnologiesare

Journalhomepage: http://telkomnika.uad.ac.id

onlyavailableattheedgeoftheforest.Unfortunately,atthemonitoringsite,wildlifeanimalsusuallylivein deepforestwherenowirelesssignalisavailableatall.

Toperformwildlifeandforestmonitoring,standalonecameratrapsareinstalledbywildlifepatrollers inthedeepforest.Afterdepletionofbatteriesthatlastforabout30–45days,cameratrapsarebroughtbackto thecampsiteforretrievingcapturedpicturesorvideostoredintheSDcard.Patrollersmustvisiteachsitein harshwalkingconditionsatleasttwice.Furthermore,ifsuspiciousorabnormaleventsoccur,itistooslowfor officerstoadoptadecisionandtakeanyactionthatdoesnotcauseseveredamagetothenaturalresources.

Newversionsofcameratrapsareequippedwithawirelesscommunicationcapabilityfordatauploadingvia3G,LTE,orWi-Fi.However,todeploythecameratraps,wirelessnetworkinfrastructureisrequiredat themonitoringsite.Extendingthecoveragesignalofthecellularnetworkisnotafeasiblesolutionduetoits restrictedinstallationandoperationcostfortheserviceproviders.

Specifically,Wi-Fimeshnetworkexhibitsadhocnetworktopologiesandisaninterestingsolutionto createanetworkinfrastructure.Additionally,Wi-Fimeshnetworkisaself-healingnetworkthatisdesignedto recovercommunicationproblemsduetobrokenlinksormalfunctionednodesbyusinganyalternativeroutes. Inforest,allnodesareevidentlyoperatedwithlimitedbatterypowerthatcanberechargedbyitsinstalledsolar powersystem.However,thesolarchargermaynotalwaysworkduetocloudyskiesortreecanopyconditions. Furthermore,batteriesreplacementrequiressignificanttimeandefforts.Therefore,itisnecessarytominimize energyusagetoprolongnetworksystemuptime.

Tominimizeenergyusage,manyroutingmanagementtechniquesareproposed,includingspectral efficiencyroutingprotocol[4],flowandpowerallocationroutingprotocol[5]–[7],load-awareroutingalgorithm[8],[9],harvestingenergymechanismbasedonenergy-efficientroutingprotocol[10]andanenergyefficientroutingprotocolwithnodemobilitysupport[11].Besides,ahybrid(proactiveandreactive)routing protocol[12]hasalsobeenproposed.Someothermechanismsincludesoftware-definedenergy-awarerouting protocol[13],[14],admissioncontrolforpreventingfastbatterydepletion[15],operatingcertainnodesinsleep mode[16],limitingcertainrouterfunctionsforaperiodofatime[17],[18],energyawaremulti-layerdesign [19]andtopologycontrolmechanism[20],[21].Forchannelmanagement,adynamicchannelassignmentfor wirelessmeshnetwork[22]orimplementingageneticalgorithmandneuralnetworktooptimizeenergy[23] arealsopresented.

Alltheaforementionedenergyawaretechniquesareperformedduringoperationalperiodafternodes areinstalledatthepredefinedlocation.Evidently,locationsofallnodesareamajorfactorforsystemenergy usage.Inappropriatenodelocationscanshortensystemlifetime.Whencertainnodesareidle,somenodes canconstituteasystembottleneckwhereinenergyisquicklydrainedandcannotbealleviatedbyanyrouting adjustmenttechniques.

Itispossibletodetermineoptimallocationsofallrouternodesoff-linebyusingabruteforcetechnique beforetheinstallationperiod.However,thecalculationtimecanbeexcessivelylong.Inarealdeployment situation,locationsofallsensornodesarecarefullyselectedbypatrollers.Duringtheinstallationperiod,the sensornodelocationsnormallyrequiresomeadjustmentsbasedonthesiteenvironmentandanimaltrails. Exactsensornodelocationsmaynotbeknownbefore-hand,thusthemeshrouterlocationfindingsmustbe completedinalimitedperiodduringtheinstallationphase.

Inthestudy,anEnergy-awarewirelessmeshnetworkdeploymentoptimizationisproposed.Themain objectiveistodetermineappropriatelocationsofallmeshroutersinashortperiodoftimebyimplementing optimizationusingtheR-module.Additionally,selectedplacementlocationsshouldmaximizethesystem lifetimeforeffectivelyoperatingandmaintainingthesystemintheforest.Hence,afterobtainingoptimization results,thesystemlifetimeandnetworkperformanceparametersofallsensorsandrouternodesareevaluated andcomparedwithbrute-forcemechanismbyusinganns-3simulator.

Relatedextantstudiesarepresentedinthenextsection.Subsequently,theproposedmechanism,problemformulationandperformanceevaluationaredescribedinsection3,section4,andsection5,respectively. Thesimulationresultsarepresentedinsection6.Finally,section7concludesthestudy.

2. RELATEDWORK

Forthewirelessmeshnetworkplacement,mostextantstudiesminimizethedeploymentcost.Forexamplein[24],themixed-integerlinearprogrammingmodeltoreducetheinstallationcostofthewirelessmesh networkisproposedbyaccountingfortrafficrouting,interference,rateadaptation,andchannelassignment.

Energy-awarewirelessmeshnetworkdeploymentusingoptimizationmechanism(AphirakJansang)

Furthermore,themechanismcanimprovethecalculationtimewiththesamenetworkperformancebyusinga heuristicalgorithm.However,energyawarenessisnotconsidered.Byextendingthealgorithmffrom[24],an enhancedalgorithm[25]usesthetrafficprofilestoturnsomenodesonandofftominimizeenergyusagewhile therequirednodetransmissionisstillsatisfied.

Later,twomathematicalmodels[26],subsequenceimprovement[24],[25],forenergy-awarewireless meshnetworkmanagementhavebeenproposedtofindthebalancebetweentheinstallationcostandenergy managementoperationcost.Thefirstmodelaimsatlocationfindingsbasedoninstallationcost.Incontrast,the secondmodelseekstominimizeenergybyturningon-offroutersduringtheoperationperiodbasedonnetwork demands.

Nevertheless,forrechargeableMRplacementwithbatterylimited,twoMRassociationmechanisms foreachnodehavebeenproposed[27].Furthermore,theenergycharginganddischargingmodelsarealso includedintheproposedoptimizationmodelstoprotectrouterenergyfrombeingdepleted.Similarly,Wang etal. [28]usedthesameconstraintsintheoptimizationmodel.However,notonlytheMRplacementbut alsotheInternetgatewayhavebeenincorporatedtominimizetheinstallationandenergyconsumptioncostsby usingthetime-divisionmultiplexingslottedmechanismfortraffictransmission.In[24]–[28],allmechanisms focusonfindingtheMRlocation.However,inthecaseoftheinstalledmeshnetwork,addingmoregateway forreducingtheenergyusagehasbeenproposed[29].Themixed-integerlinearprogrammingandagreedy algorithmarepresentedandcompared.Theresultsshowthatthemixed-integermechanismgivestheoptimal result.Meanwhile,only5%gainstheoptimalsolutionforthegreedyalgorithmwithonlyonepercentofthe computationaltime.

Aheuristicmethod[30]isproposedtofindthemaximumnumberofnoninterferinglinksforcreating concurrenttransmissionpathsinafixedinstallednodeenvironmentscenario.Theenergy-savingcomponent wasnotaddressedinthisstudy.Twomixed-integerlinearprogrammingandalocalsearchalgorithmtodeterminetheoptimalgatewaycandidatelocation[31]isproposedwiththeassumptionofunlimitedpowersource formeshrouter.

Thenodeplacementmechanismsbyusingintelligenthybridsystemconsistingofparticleswarmoptimization,hillclimbing,andsimulatedannealing[32]isproposed.Byfixingthenumberofmeshrouters, theroutersareplacedforoptimalcoverageforallclientnodesbasedoncomputationaltimeconcern.However,performanceevaluationisomitted.Anothermeshrouterplacementisproposedbyimplementingthe electromagnetism-likemechanism(EM)metaheuristictechnique[33].Thestudyfocusesonnetworkconnectivityofallclientnodes.Energyharvestismentionedinafuturestudy.

Theoptimizinggoalofmostpreviousresearchinitiallyfocusesonminimizingtheinstallationcost. Theenergyawarenessisthenextinterestingobjective.However,theproposedprotocolalsofocusesonmaximizinglifetimebesidesenergyawareness.RelatedresearchissummarizedasshowninTable1.

Table1.Relatedworkcomparison RelatedworkEnergy-waredesignRechargableenenrgyOptimizationmethodMinimizeinstallationcostMaximizelifetime Amaldi etal.[24]

Capone etal.[25]

Boiardi etal.[26]

Huan etal.[27]

Wang etal.[28]

Sakamoto etal.[32]

Sayad etal.[33]

3.

PROPOSEDMECHANISM

Thescopeofthestudyistodetermineappropriatemeshrouter(MR)locationsinaforestinashort periodoftime.Allsensornodesaretermedasmeshclient(MC)includingcameratrapsthatareassumedto beplacedintheforestforonlyuploadinginformationtoMR,whichiseventuallyforwardedtothegateway. Thelocationsofsensingdevices,MCs,aredeterminedbythepatrolofficerinarangeoffewkilometers.

TELKOMNIKATelecommunComputElControl,Vol.21,No.1,February2023:26–40

Figure1.Humanhabitatandforestarea

Cellularservicesarenotavailable.Furthermore,sensinginformationcanbevariedbyafewbytesinvolving temperatureandhumidityvaluestocaptureimagesorvideoswithlargefilesize,andtherebynecessitatinghigh datatransmissionrates.Evidently,therequiredscenariocannotbesupportedbytheLoRAtechnologythatis designedforlongrangecommunicationswithlowpowerandaverylimitedtransmissionrate.Thegatewayis locatedattheforestedgenearavillage,campsite,orhumanhabitatwhereInternetaccessandelectricityare available.ThedeploymentscenarioexamplemapisshowninFigure1.

3.1. Proposedsystemmodel

Inthestudy,eachMRisoperatedonlybyastandalonebatterywithoutsolarsystem.Hence,theMR lifetimedependsuponitsoperationandtransmissionactivities.WeassumethatallMRsareinstalledwithsame batterycapacity.Inordertoevaluatethescenario,Wi-Ficommunication(IEEE802.11g,2.4GHz,54Mbps) technologyisselected.

Themonitoringareaisdefinedas M × M meters,whichisdividedintosmallgridcellsbasedonits Wi-Ficommunicationcapabilities.Inthestudy,theplacementscenarioexampleisshowninFigure2(a)where Mequals600m.TheMCcanbeplacedanywhereinthemonitoringareathatisthenmappedintothecenter ofaMCgridcell, 50m × 50m insize,asshowninFigure2(b).Afterperformingtheoptimization,allMR locationscanbefoundandplacedinthecenterofaMRgridcell, 100m × 100m insize,asshowninFigure 2(c).ItisnotedthattodecreasetheMRlocationsearchspaceinourproposedoptimizationmodel,theMR gridcellistwicetheMCgridcell.

ByassumingthatthetransmissionrangeforeachsensingnodetoMRandviceversaissetto250 mfromthecenter,itapproximatelyequalstothenext4MCgridcells.Conversely,thetransmissionrange betweenMRsissetto550m,whichapproximatelycorrespondstothenext5MRgridcells.Hence,with respectto 600m × 600m,fourMRscanextendcoverageinalltheareas.However,theadditionofonemore MRincreasesflexibilityforMRlocationadjustments.

TheconstraintfortheMRplacementisthateachMCmustbecoveredbyatleastoneMRtoensure connectivity.WeassumethatallsensingdevicesareequippedwithanSDcardandareusedtoperiodically uploadinformationataconstantbitrate(CBR).Furthermore,physicaldatatransmissionrateisconsideredas afixedvalueindependentofthecommunicationdistance.

Theproposedmodelisformulatedasamixedintegerlinearprogramming.Inordertodeterminethe optimizedsolution,Rprogramwiththelinprogpackage[34]isused.Fromanetworkviewpoint,thederived locationsfromthemodelareevaluatedwithns-3(networksimulator3)[35]fornetworkperformanceand batteryutilizationthataresubsequentlycomparedwiththebrute-forcemechanism.

Energy-awarewirelessmeshnetworkdeploymentusingoptimizationmechanism(AphirakJansang)

Figure2.Devicemappinginthemonitoringareain(a)sensingdeviceandgatewaylocations,(b)MCgridcell (50m × 50m)and(c)MRgridcell(100m × 100m)

4. PROBLEMFORMULATION

4.1. Datapre-processing

ThelocationsofallMCsandgatewayareusedastheinputoftheproposedmodelthatissubsequently mappedtogridcellsintheoptimizationmodelparameters,asshowninFigure2(a).Let C = {1,...,c}, R = {1,...,r},and G = {1,...,g} bethesetofMC,MR,andgatewaygridcells,respectively.EachMC locationisassignedsuchthatitisamemberofacorrespondingMCgridcell.InMCgridcell i pointof view,thereachableparameter aij ofeachMRgridcell j thatcommunicateswith(fromeachMClocationto reachableMRlocation)each i ∈ C,j ∈ R is: aij = 1, MCgridcell i coveredbyMRgridcell j 0, otherwise (1)

1

0

(2)

Allthereachablecellsfromgatewayarecalculatedandstoredasgatewayrangeparameter wjg j ∈ R,g ∈ G: wjg =

         1, ifMRinlocation j andgatewayinlocation g areineachcommunicationrange 0, otherwise

(3)

Next,wedefineadecisionvariable.TheMRassignmentvariable zj foreach j ∈ R: zj = 1, ifMRisinstalledatlocation j 0, otherwise (4)

TheMCassignmentvariable xij foreach i ∈ C,j ∈ R: xij = 1, ifMCatlocationi connectwithMRatlocation j 0, otherwise (5)

TheMRconnectionvariable yjl foreach j,l ∈ R: yjl = 1, ifMRatlocation j connectwithMRatlocation l 0, otherwise (6)

Thegatewayassignmentvariable Yjg foreach j ∈ R,g ∈ G: Yjg = 1, ifMRatlocation j connectwithgatewayatlocation g 0, otherwise (7)

4.2. Optimizationmodel

Fromtheobjectivefunction,theoptimallocationforallspecificnumberofMRsthatmakesthelongest operationlifetimeiscalculated.ThesystemlifetimethatisdefinedasoneofMRsisdepleted.Givencertain constraints,thesystemlifetime T ismaximized(orminimizingtheinverseof T )(8).

The xij isabinaryoperatorthatcontrolstheassociationbetweenMRandMC.IfMC i canconnect withMR j,thesumof xij foreachMC i shouldbeatleast1.Therefore,constraints(9)ensuresthatMC i can associatewithonlyoneMR j.Whileconstraints(10)arecoherenceconditionstoguaranteethatMC i canbe associatedwithMR j onlyifthereexistsMRat j iswithincommunicationrangeofMCat i Constraints(11)isdefinedtheflowbalanceatMR j,whichistheinbounddataratethatneedstobe equaltotheoutbounddatarate.Let di beademandeddataratethatMC i tosendtoanyMRs.Let Frlj be thedataratethatMR l requirestosendtoMR j andlet Fgjg bethedataratethatMR j requirestosendto gateway g.Theterm i di xij isthetotalneededdataratetotransmittoassociatedMR j l Frlj isthe totaldataratethatMR i neededtosendtoMR j.ItisthetotalinboundtrafficforMR j.Incontrast, l Frjl and g Fgjg arethetotaltrafficofMR j senttootherMRsandgateways,respectively,whichistheoutbound direction.

Let Capjl bethecapacityofthewirelesslinkbetweenMR j andMR l.Constraints(12)ensurethat thelinkbetweenMR j andMR l existsandthesumoftheoutbounddatarateandinbounddataratebetween MR j andMR l mustnotexceedthelink’scapacity.Constraints(13)–(15)definetheexistenceofawirelesslink betweenMR j andMR l,whichdependsontheexistenceofMRatlocation i,location l andcommunication rangevariable bjl.Constraints(16)ensurethatthenumberoflinksbetweenMRtoMRandMRtogateways doesnotexceedthelink’sdegree.Thus,thesumofanyMR j’slinks(MRtoMR,MRtogateways)mustnot surpassits Degj ,where Degj bethemaximumdegreeofMR j

SincetheconnectionfromanyMRstothegatewayisonlyuplinkdirection.Thus,constraints(17) ensuresthatlinkfromMR j togateway g mustexistandlimitby Capjg,andonlyoutboundtrafficfromMR j tothegateway g isconsidered.Let Capjg bethecapacityofthewirelesslinkbetweenMR j tothegateway g.Constraints(18)definetheexistenceofawirelesslinkbetweenMR j andgateway g,whichdepends

Energy-awarewirelessmeshnetworkdeploymentusingoptimizationmechanism(AphirakJansang)

ISSN:1693-6930

ontheexistenceofMRatlocation i,thegatewayatlocation g,andthecommunicationrangevariable wjg. Constraints(19)ensurethatthetotalnumberoflinksfromanypossibleMRstogateway g mustnotexceedits degree,asdenotedby Degg

Let Enerj betheinitialenergyofMR j.Let T denotethesystemlifetimethatstartsfromthestartup timeuntilthefirstMRrunsoutofbattery.ThenumberofrequiredMRsisdefinedbyvariable numr.Given thatenergyconsumptiononeachMRdependsonitsdatarateandsystemlifetime T .Theoutbounddatarate forMR j issummingupthedataratefromotherMRstoMR j (definedas l Frlj )anditsrequireddatarate togateway g (definedas g Fggj ).Then,theconstraint(20)isusedtoensurethattheenergyusageofeach MR j ( l Frlj + g Fggj multipliedbytime T )willnotexceedits Enerj .Constraints(21)ensurethatthe totalnumberofselectedMRs( j zj )mustnotexceed numr

Fromtheequationshownabove,therearemanypossiblesetsofMRlocations(zj )thatarequalified byallconstrains.However,theobjectiveoftheproposedmechanismistofindforthebestoutcomeof zj whichyieldsthelongestsystemlifetime.Therefore,theoptimizationsolverisrequiredforobtaining zj ,which deliversthebestresult.

Maximize: T (8)

Subjectto: j

xij =1, ∀i ∈ C (9)

xij ≤ aij · zj , ∀i ∈ C, ∀j ∈ R (10)

i di xij + l Frlj = l Frjl + g Fgjg, ∀j ∈ R (11)

Frjl + Frlj ≤ Capjl yjl, ∀j,l ∈ R (12) yjl ≤ bjl, ∀j,l ∈ R (13)

yjl ≤ zj , ∀j,l ∈ R (14) yjl ≤ zl, ∀j,l ∈ R (15)

l yjl + g

Yjg ≤ Degj , ∀j ∈ R (16)

Fgjg ≤ Capjg Yjg, ∀g ∈ G, ∀j ∈ R (17)

Yjg ≤ wjg, ∀g ∈ G, ∀j ∈ R (18) j Yjg ≤ Degg, ∀g ∈ G (19)

( l Frlj + g Fggj ) T ≤ Enerj , ∀j ∈ R (20) j zj ≤ numr, ∀j ∈ R (21)

5. PERFORMANCEEVALUATION

Inthestudy,fivecasesofvariousMCnodes(i.e.15,20,25,30,and45)areevaluated.Ineachcase, threereplicationsoftheMClocationsarerandomlygeneratedwithuniformdistribution.Thegatewaylocation isassumedasfixedforallcases.Forsystemlifetimeevaluation,eachbrute-forcesolution(fromallpossible MRlocations)foreachcaseissimulatedandcomparedwiththecorrespondingoptimizationsolution.

5.1. Extractingoptimizationresults

BeforetheproposedoptimizationmodelcandeterminetheresultsofpotentialMRlocationsonthe map,thebasicinformationfromtheactualmap,suchastheMCs’locationsandthelocationofthegateway usedtorelaydata,mustfirstbegathered.Thecollecteddatawillthenbeconvertedintothemodel’sinitial vectorvariables,suchasmatrix aij forMClocations, wjg forgatewaylocations,whichisdescribedinsection 4.1.Next,initializethemodel’svariablesbycalculatingallpossibleMRlocationpairsinthedeployment areafromvector bjl andgeneratingthemintothegridcellasmatrix wjg.Atthesametime,thematrix Yjg isgeneratedfromvector wjg tokeepallpossibleMRlocationsthatcancommunicatewithgatewayg.The otherrequiredvectorvariables(e.g. zj , xij ,and ylj )andallconstraintsaresetupbeforerunningthelinear optimizationsolver.Subsequently,theprogramforsolvinglinearequationswillthenstarttosearchforthe possiblesolutionsthatmakethemostextendedsystemlifetimewhilesatisfyingallconstraints.Thesoftwareis writteninRandsolveslinearprogrammingtaskswiththe linprog package.Afterobtainingtheoptimization resultfortheMR’slocationsingridcellformat,itwillconvertthembacktotheiractualmaplocationsbefore beingusedintherealimplementation.TheflowchartinFigure3illustratestheprocessofextractingtheresults fromtheproposedoptimizationmodel.

Start

Collect required information on the map (e.g. locations of gateway and MCs)

OptimizationModel

Convert locations into model's grid cells

Initial model's variables and constrained

Optimize MR's locations with linprog

Collect All MR's grid cells locations which is satisfed constrained

Convert All MR's grid cells into map's locations

End

Figure3.Theprocessofextractingtheresultsfromtheproposedoptimizationmodel

Energy-awarewirelessmeshnetworkdeploymentusingoptimizationmechanism(AphirakJansang)

5.2. Simulationsetup

Tocomparethenetworkperformancebetweentheproposedandbrute-forcemechanisms,theoptimizationresultsfromtheR-programaresimulatedbyns-3simulator.Thesimulationfieldareaisdefinedas 600 × 600 m2.TheMCgridcellaredefinedas 50 × 50 m2 forplacingeachMC.Conversely,theMRgrid cellof 100 × 100 m2 isusedforMRplacement.TheMCtrafficpatternsforuploadingdatatothegateway aredefinedataconstantbitrateof1Mbps.AllMRsaredeployedwiththesameinitialcapacitiesandenergy dischargemodel.Specifically,IEEE802.11gwithfixedpropagationdelaywiththedefaultrangepropagation lossmodelisusedthroughoutthesimulation.Wemodifiedtheoptimizedlinkstateroutingprotocol(OLSR)to supportanenergy-awareroutingprotocolbyconsideringtheremainingenergyineachpathbeforetransmitting thedata.Thetotalsimulationtimecorrespondsto80s.EachMCautomaticallystartsuploadingatthe40th secondafterthesimulationisstarted.ThesimulationparametersarelistedinTable2.

Table2.Parameterofsimulation ParametersValue

Fieldsize 600 × 600 m2

Client’sgridsize 50 × 50 m2 Router’sgridsize 100 × 100 m2

Communicationrange(router-client)250m

Communicationrange(router-router)550m Client’sapplicationCBRat1MbpsperMC

Energymodelns-3energymodel

Initialenergy80J WirelessstandardIEEE802.11g/54Mbps DelaymodelConstantspeedpropagation LossmodelRangepropagation RoutingprotocolOLSRwithenergy-awaresupport Totalsimulationtime80s

Applicationstarttime40ths

Numberofclientspercase15/20/25/30/45 Numberofexperimentspercase3 Numberofrouter5 SearchingmethodOptimization/brute-force

5.3. EnergyUsageModel

Thedefaultns-3energymodelisusedthroughoutthesimulation.Theenergyusagesarebasedon itsactivities,namelytransmit,receive,oridle,asshowninTable3.EachMRstopsworkingifitrunsoutof energy.

Table3.Energymodelusedinns3[35]

ActivityCurrentusage(ampere) Idle0.273 Receive0.313 Transmit1.800

6. SIMULATIONRESULTS

Theproposedmechanismisevaluatedforthecomputationtimeandnetworkperformancebycomparingitwiththebrute-forcemethod.AsshowninFigure4,thecomputationaltimes(includingthesimulation times)forvariousMCsoftheproposedoptimizationarealmostconstant.Furthermore,thebrute-forcemechanismcomputationtimeissignificantlyvariedandsignificantlyhighwhencomparedtotheproposedmechanism.Withrespecttonetworkperformance,thesystemlifetimes,asshowninFigure5,forbothmechanisms arealmostidentical,andtheybothrunoutofbatteryatapproximately65thsecond.Fortheaveragethroughput andpacketdeliveryratio,showninFigures6and7,forthelownumberofclients,thepredefinedcondition oftheoptimizationmightrestricttheallowabledegreeofMCconnectingforeachMR.Therefore,oncethe increasingnumberofclientsreaches30,bothbrute-forceandoptimizationmechanismsperformthesamedue tothelimitedchoicesofMRlocationforcoveringallMCs.However,inFigure8,theaveragedelayvariation

TELKOMNIKATelecommunComputElControl,Vol.21,No.1,February2023:26–40

TELKOMNIKATelecommunComputElControl

ofthebrute-forcemechanismformanyconnectingclientsshowsasignificantvariationduetotheunbalanced connectingdegreeforeachMR.

FromFigure9,anexampleof25clientsrandomlyscatteredonthemapispresented.Theresultsof thedifferentmethodsfordeterminingtheplacementof5MRlocationsareshowninFigure10.Theoptimizationsolution(showninFigure10(a)andFigure10(b))yieldsmorescatterMRlocationsthanthebrute-force mechanism(showninFigure10(c)andFigure10(d))becausetheoptimizationmechanismtriedtolimitthe degreeofconnectingMCforeachMR,whichismoreapparentinthecaseofthehighernumberofrequired MRs.

Figure4.Comparisonofcomputationtimebetweenmethods

Figure5.Comparisonoflifetimebetweenmethods

Energy-awarewirelessmeshnetworkdeploymentusingoptimizationmechanism(AphirakJansang)

Figure6.Comparisonofaveragethroughputbetweenmethods

Figure7.Comparisonofpacketdeliveryratiobetweenmethods

Figure8.Comparisonofaveragedelaybetweenmethods

TELKOMNIKATelecommunComputElControl,Vol.21,No.1,February2023:26–40

Figure9.Clientlocations

Figure10.ComparisonofMRplacementbetweenbrute-forceandoptimizationmethods(for25clients)in(a) clientconnection:brute-forcemethod,(b)routerconnection:brute-forcemethod,(c)clientconnection: optimizationmethod,and(d)routerconnection:optimizationmethod Energy-awarewirelessmeshnetworkdeploymentusingoptimizationmechanism(AphirakJansang)

Theinter-arrivaltimeofthecaseof25MCs(fromFigure10)isshowninFigure11.Giventhe cumulativedistributionfunction(CDF)valueof0.80,theperformanceoftheoptimizationmethodsexceeds thatofthebrute-forcemethod.Additionally,withrespecttodifferentnumberofMCnodes,theaverageof inter-arrivaltimeat80thpercentileisshowninFigure12.Theperformanceoftheproposedmethodslightly exceedsthatofthebrute-forcemethodforahighnumberofMCnodes.Whencomparedtothebrute-force mechanism,thenetworkperformanceobtainedbyourproposedmechanismaresignificantlyidenticalwhile thecomputationtimesignificantlyoutperformsthebrute-forcemechanism. 0.8 CDF

Figure11.Inter-arrivaltimeof25clients

Figure12.Theaverageofinter-arrivaltimeat80thpercentile

7. CONCLUSIONS

Inthestudy,weproposedtheoptimizedwirelessrouterplacementmethodwithenergyawarefor wirelessmeshnetworkinaruralarea.Ouroptimizationgoalinvolvesachievingthelongestlifetimeofthe systemcomparedwithbrute-forcemethod.Thesimulationresultsindicatethattheoptimizationmethodyields significantlylesscomputationtimewhileobtainingthesameresultsintermofnetworkparameters.This issignificantlycrucialintherealdeploymentsituationwheresensornode(client)locationsaremandatory adjustedbasedontheenvironmentalstatusortherecommendationofthepatrollers.Eachadjustmentrequires

anewcalculation.Therefore,theproposedmechanismbecomesmoredesirableduetoitsshortcomputation time.Furthermore,theheuristicapproachcanbeimplementedforbetterperformance.

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BIOGRAPHIESOFAUTHORS

AphirakJansang receivedhisB.Eng.,M.Eng.andD.Eng.degreesinComputerEngineeringfromKasetsartUniversity,Bangkok,Thailand,in2000,2003and2012,respectively.Heis currentlyanAssistantProfessorattheDepartmentofComputerEngineering,KasetsartUniversity, Thailand.HeisalsoateamleaderofIntelligentWirelessNetworkGroup(IWING).Hisresearch interestsincludewirelessnetworks,resourcemanagementandqualityofservicesupport.Hecanbe contactedatemail:aphirak.j@ku.ac.th.

ChayathornSimasathien receivedtheB.Eng.andM.Eng.degreeinComputerEngineeringfromKasetsartUniversity,Bangkok,Thailand,in2014and2020respectively.Heis alsoateammemberofIntelligentWirelessNetworkGroup(IWING).Heiscurrentlyworkingat KASIKORNBusiness-TechnologyGroup(KBTG),THAILAND.Hecanbecontactedatemail: chayathorn.s@ku.th.

AnanPhonphoem

receivedhisPh.D.inElectricalandComputerEngineering(2000) fromUniversityofMassachusettsAmherst,M.S.inComputerEngineering(1996)fromUniversity ofSouthernCalifornia(USC),andB.Eng.inElectricalEngineering(1990)fromPrinceofSongkla University,Thailand.HeiscurrentlyanAssociateProfessorandthedirectoroftheIntelligentWirelessNetworkGroup(IWING)atComputerEngineeringDepartment,KasetsartUniversity,Thailand. Hisresearchinterestsincludewirelessnetworks,adhocnetworks,performanceevaluation,andprotocoldesignandanalysis.Hecanbecontactedatemail:anan.p@ku.ac.th.